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Proceedings Paper

Probabilistic modelling of diabetic nephropathy progression
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Paper Abstract

Diabetic Nephropathy (DN) progression is stratified into several stages with different levels of proteinuria, albuminuria, and physical characteristics as observed by pathologists. These physical changes are primarily visible within a patient’s glomeruli which function as filtration units for blood returning for oxygenation. As DN stage increases, it is possible to observe the thickening of the glomerular basement membrane, expansion of the mesangium, and development of nodular sclerosis. Classification of different stages of DN by pathologists is based on semiqualitative assessments of these characteristics on an individual glomerulus basis. Being able to probabilistically infer stage membership of individual glomeruli based on a combination of easily observable and hidden image features would be an invaluable tool for furthering our understanding of the drivers of DN progression. Markov Particle filters, included in the bnlearn package in R, were used to query a Bayesian Network (BN) constructed using the structural Hill-Climbing algorithm on a set of glomerular features. These features included both traditional characteristics such as glomerular area and number of mesangial nuclei as well as more abstract features derived from Minimum Spanning Trees (MST) to quantify spatial distribution of mesangial nuclei. Our results using images from multiple institutions suggest that these abstract features exercise a variable influence on DN stage membership over the course of disease progression. Further research incorporating clinical data will give nephrologists a “white box” visual of quantitative factors present in DN patients.

Paper Details

Date Published: 16 March 2020
PDF: 10 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132014 (16 March 2020); doi: 10.1117/12.2549171
Show Author Affiliations
Samuel Border, Univ. at Buffalo (United States)
Kuang-Yu Jen, Univ. of California, Davis (United States)
Washington LC dos-Santos, Federal Univ. of Bahia (Brazil)
John Tomaszewski, Univ. at Buffalo (United States)
Pinaki Sarder, Univ. at Buffalo (United States)

Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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